The invention relates generally to the field of biosurveillance. More specifically, embodiments of the invention relate to systems and methods for mediating anomalies found in health data to health alerts.
With the ever-increasing availability of various kinds of medical data in electronic format, including hospital records, laboratory results, pharmacy sales records, physicians' notes and records, up to and including the electronic health records (EHR), it has become possible to engage in large-scale biosurveillance in real or near-real time. Biosurveillance might be undertaken for several different reasons, including early detection and characterization (geographic, demographic) of disease outbreaks, and detection of bioterrorism, of environmental health trends, and of changes in chronic disease patterns. The biosurveillance activities further support resource allocation and planning at healthcare providers and at public health institutions. The key objective is to explore medical data, including characterizing anomalies (detected in the data by one or more anomaly detection methods, some of which are described elsewhere in invention disclosures). This exploration is by graphical display, through drill down into highly multivariate detail data, and includes the ability to compare data with earlier periods. The data are typically structured across a number of dimensions, including time, geography, and those inherent in the medical context, for example the group of laboratory tests ordered, the hospital department visited, or the syndrome of the patient visiting the emergency department. To do this effectively, we need a tool or tools to help investigators query, visualize, explore, and ultimately understand large volumes of medical data.
Biosurveillance is the monitoring of the biological and health status of a population for changes against norms established by historical data for that population including changes against trends or established cyclic patterns in the data. An anomaly or aberration occurs when the status is markedly different from the associated norm. When this occurs, a system for biosurveillance may generate an anomaly notification, or what is commonly referred to as an anomaly. Presently, the ability of public health care systems to monitor health data and trends in the data so as to identify anomalies and to manage the anomaly notifications tends to be antiquated and slow.
An anomaly in health data can be characterized by anomaly variables. Anomaly variables include location—for example, one or more towns, ZIP codes, cities, counties, states, etc.; medical context—for example, influenza, West Nile virus, gastro-intestinal complaints, etc.; time frame—one or more days; additional covariates—for example, age and gender; measures of severity; the algorithm by which the anomaly was discerned, and security content.
In surveillance of health data, numerous conditions lead to an anomaly notification, ranging from a single patient with a specific condition, to a pattern in the data detected by a statistical method. An anomaly notification is not per se actionable, but if one or more anomaly notifications are followed by a health alert, then that alert can be a trigger for various health and public safety measures. In determining how to respond to anomaly notifications and whether to issue an alert, various experts are involved. The experts may include data experts, statistical experts, epidemiologists, public health officials, and others.
Both public and private health care organizations face the challenge of developing an effective system for anomaly detection and management of anomaly notifications. For example, the Centers for Disease Control (CDC) have stated their intention to incorporate multiple anomaly detection algorithms into their surveillance system known as BioSense.
BioSense provides an integrated national view for electronic biosurveillance. The BioSense application augments local or regional surveillance systems with additional data, jurisdictional views, and analytic techniques to further characterize an outbreak or event. The data may also be combined with other data sources to provide a more complete picture of the health status of that geography or metropolitan area. While the CDC makes use of a health alert network, they do not presently have a case manager system.
New computer information systems are actively being developed to monitor data from various sources and of various types, from emergency room admissions information to over-the-counter (OTC) sales of pharmaceuticals. These systems look for patterns that might not be apparent to individual doctors or pharmacists, but might indicate an event of health concern such as the beginnings of a disease outbreak.
The need for consolidated regional and national public safety data has been expressed repeatedly by government agencies including the CDC, the Department of Defense, Homeland. Security, and the Department of Health and Human Services, among others. The National Association of State EMS (Emergency Medical Systems) Directors has called for biosurveillance systems to be implemented throughout the healthcare and public safety systems.
Data management and combining data from multiple sources are important components in a biosurveillance system. For example, complete EMS data is missing at the regional and state level. Detection of acute or covert terrorist attacks requires an effective linking of data from a variety of sources, and an effective public health response will depend on the timeliness and quality of communication.